Ukraine's HIV epidemic is the most severe in Europe, with 70% of cumulative HIV cases among people who inject drugs (PWIDs) with recent evidence suggesting transition to a generalized epidemic. Despite recommendations by international agencies (WHO, UNODC, UNAIDS), current HIV prevention and treatment efforts in Ukraine remain woefully inadequate. A new cost-effectiveness study that incorporates a combination of interventions, including newly emerging ones like pre-exposure prophylaxis (PrEP) among PWIDs is urgently needed to provide a better understanding of the scale-up of various interventions and help allocate scarce resources more effectively. In Ukraine and elsewhere, the cost effectiveness analyses (CEA) are further complicated by a lack of reliable data and measurement inconsistencies inherent when using Respondent Driven Sampling (RDS), a sampling method widely used in Ukraine and globally to study PWID. To address these critical gaps, the mentored research projects will include two different modeling studies, which are connected through the emphasis on RDS and CEA. In the first study the goal will be to analyze the effect of non-response, selection bias, missing data and measurement error in RDS through a focus on networks analysis and recruitment, while the second project will explore the extent to which the bias in RDS data influences CEA of a combination of current and new treatment and prevention interventions. Findings from these projects will contribute to better understanding of and improvements in the performance of RDS allowing for less biased statistical inference, as well as more accurate secondary analysis including CEA in Ukraine and other countries. The candidate, Dr. Zelenev, is uniquely poised to perform this work because of his quantitative background in sociology and economics and his high productivity in public health research, exhibited over a two year post-doc at Yale. Over 5 years, he will achieve his career goals and professional objectives to: 1) gain experience in survey design, new modeling methods and network data analysis in order to evaluate recruitment and network biases, HIV transmission dynamics, and enrollment in treatment among PWID; 2) develop expertise in substance abuse, cost-effectiveness and evaluation of evidence-based interventions (EBIs) for HIV treatment and prevention; and 3) develop an independent career path in epidemiological and mathematical modeling research focusing on the interface between HIV, addiction, and implementation science. To achieve these goals, he has assembled a stellar interdisciplinary team of mentors with expertise in HIV, substance abuse, epidemiology, mathematical modeling, cost effectiveness and intervention analysis. Under their guidance, he will complete relevant didactic work and attend seminars and conduct research to apply the skills needed to become an independent researcher in HIV, substance abuse, mathematical modeling and epidemiology within the rich intellectual environment available at Yale University.